Overview

Dataset statistics

Number of variables21
Number of observations3321
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory545.0 KiB
Average record size in memory168.0 B

Variable types

Categorical3
Numeric16
Boolean2

Alerts

State has a high cardinality: 51 distinct valuesHigh cardinality
Phone has a high cardinality: 3321 distinct valuesHigh cardinality
VMail Message is highly overall correlated with VMail PlanHigh correlation
Day Mins is highly overall correlated with Day ChargeHigh correlation
Day Charge is highly overall correlated with Day MinsHigh correlation
Eve Mins is highly overall correlated with Eve ChargeHigh correlation
Eve Charge is highly overall correlated with Eve MinsHigh correlation
Night Mins is highly overall correlated with Night ChargeHigh correlation
Night Charge is highly overall correlated with Night MinsHigh correlation
Intl Mins is highly overall correlated with Intl ChargeHigh correlation
Intl Charge is highly overall correlated with Intl MinsHigh correlation
VMail Plan is highly overall correlated with VMail MessageHigh correlation
Int'l Plan is highly imbalanced (54.0%)Imbalance
Phone is uniformly distributedUniform
Phone has unique valuesUnique
VMail Message has 2401 (72.3%) zerosZeros
CustServ Calls has 695 (20.9%) zerosZeros

Reproduction

Analysis started2023-02-14 23:03:47.829325
Analysis finished2023-02-14 23:04:13.387547
Duration25.56 seconds
Software versionpandas-profiling v3.6.6
Download configurationconfig.json

Variables

State
Categorical

Distinct51
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Memory size26.1 KiB
WV
 
106
MN
 
84
NY
 
82
AL
 
80
WI
 
78
Other values (46)
2891 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters6642
Distinct characters24
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowKS
2nd rowOH
3rd rowNJ
4th rowOH
5th rowOK

Common Values

ValueCountFrequency (%)
WV 106
 
3.2%
MN 84
 
2.5%
NY 82
 
2.5%
AL 80
 
2.4%
WI 78
 
2.3%
OR 78
 
2.3%
OH 78
 
2.3%
WY 77
 
2.3%
VA 76
 
2.3%
CT 74
 
2.2%
Other values (41) 2508
75.5%

Length

2023-02-14T18:04:13.587431image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
wv 106
 
3.2%
mn 84
 
2.5%
ny 82
 
2.5%
al 80
 
2.4%
wi 78
 
2.3%
or 78
 
2.3%
oh 78
 
2.3%
wy 77
 
2.3%
va 76
 
2.3%
ct 74
 
2.2%
Other values (41) 2508
75.5%

Most occurring characters

ValueCountFrequency (%)
N 732
 
11.0%
A 684
 
10.3%
M 611
 
9.2%
I 510
 
7.7%
T 409
 
6.2%
D 379
 
5.7%
C 355
 
5.3%
O 345
 
5.2%
W 327
 
4.9%
V 320
 
4.8%
Other values (14) 1970
29.7%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 6642
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
N 732
 
11.0%
A 684
 
10.3%
M 611
 
9.2%
I 510
 
7.7%
T 409
 
6.2%
D 379
 
5.7%
C 355
 
5.3%
O 345
 
5.2%
W 327
 
4.9%
V 320
 
4.8%
Other values (14) 1970
29.7%

Most occurring scripts

ValueCountFrequency (%)
Latin 6642
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
N 732
 
11.0%
A 684
 
10.3%
M 611
 
9.2%
I 510
 
7.7%
T 409
 
6.2%
D 379
 
5.7%
C 355
 
5.3%
O 345
 
5.2%
W 327
 
4.9%
V 320
 
4.8%
Other values (14) 1970
29.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 6642
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
N 732
 
11.0%
A 684
 
10.3%
M 611
 
9.2%
I 510
 
7.7%
T 409
 
6.2%
D 379
 
5.7%
C 355
 
5.3%
O 345
 
5.2%
W 327
 
4.9%
V 320
 
4.8%
Other values (14) 1970
29.7%

Account Length
Real number (ℝ)

Distinct227
Distinct (%)6.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean100.54556
Minimum0.57
Maximum243
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size26.1 KiB
2023-02-14T18:04:13.683459image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0.57
5-th percentile33
Q173
median100
Q3127
95-th percentile167
Maximum243
Range242.43
Interquartile range (IQR)54

Descriptive statistics

Standard deviation40.328888
Coefficient of variation (CV)0.40110066
Kurtosis-0.078292479
Mean100.54556
Median Absolute Deviation (MAD)27
Skewness0.058695124
Sum333911.79
Variance1626.4192
MonotonicityNot monotonic
2023-02-14T18:04:13.805802image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
105 43
 
1.3%
87 42
 
1.3%
101 40
 
1.2%
90 39
 
1.2%
93 39
 
1.2%
86 38
 
1.1%
116 37
 
1.1%
100 37
 
1.1%
95 37
 
1.1%
99 36
 
1.1%
Other values (217) 2933
88.3%
ValueCountFrequency (%)
0.57 1
 
< 0.1%
0.75 1
 
< 0.1%
0.84 1
 
< 0.1%
1 8
0.2%
1.07 1
 
< 0.1%
1.11 1
 
< 0.1%
1.17 1
 
< 0.1%
1.18 1
 
< 0.1%
1.21 1
 
< 0.1%
1.28 1
 
< 0.1%
ValueCountFrequency (%)
243 1
 
< 0.1%
232 1
 
< 0.1%
225 2
0.1%
224 2
0.1%
221 1
 
< 0.1%
217 2
0.1%
215 1
 
< 0.1%
212 2
0.1%
210 2
0.1%
209 3
0.1%

Area Code
Real number (ℝ)

Distinct6
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean435.24869
Minimum4.08
Maximum510
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size26.1 KiB
2023-02-14T18:04:13.882605image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum4.08
5-th percentile408
Q1408
median415
Q3510
95-th percentile510
Maximum510
Range505.92
Interquartile range (IQR)102

Descriptive statistics

Standard deviation51.295788
Coefficient of variation (CV)0.11785398
Kurtosis20.707154
Mean435.24869
Median Absolute Deviation (MAD)7
Skewness-1.9703406
Sum1445460.9
Variance2631.2579
MonotonicityNot monotonic
2023-02-14T18:04:13.965243image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
415 1640
49.4%
510 834
25.1%
408 832
25.1%
4.15 9
 
0.3%
4.08 3
 
0.1%
5.1 3
 
0.1%
ValueCountFrequency (%)
4.08 3
 
0.1%
4.15 9
 
0.3%
5.1 3
 
0.1%
408 832
25.1%
415 1640
49.4%
510 834
25.1%
ValueCountFrequency (%)
510 834
25.1%
415 1640
49.4%
408 832
25.1%
5.1 3
 
0.1%
4.15 9
 
0.3%
4.08 3
 
0.1%

Phone
Categorical

HIGH CARDINALITY  UNIFORM  UNIQUE 

Distinct3321
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size26.1 KiB
382-4657
 
1
361-9839
 
1
379-4372
 
1
336-3738
 
1
380-2600
 
1
Other values (3316)
3316 

Length

Max length8
Median length8
Mean length8
Min length8

Characters and Unicode

Total characters26568
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3321 ?
Unique (%)100.0%

Sample

1st row382-4657
2nd row371-7191
3rd row358-1921
4th row375-9999
5th row330-6626

Common Values

ValueCountFrequency (%)
382-4657 1
 
< 0.1%
361-9839 1
 
< 0.1%
379-4372 1
 
< 0.1%
336-3738 1
 
< 0.1%
380-2600 1
 
< 0.1%
345-4473 1
 
< 0.1%
380-9990 1
 
< 0.1%
411-1045 1
 
< 0.1%
413-5306 1
 
< 0.1%
417-6906 1
 
< 0.1%
Other values (3311) 3311
99.7%

Length

2023-02-14T18:04:14.047662image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
382-4657 1
 
< 0.1%
350-2565 1
 
< 0.1%
343-4696 1
 
< 0.1%
358-1921 1
 
< 0.1%
375-9999 1
 
< 0.1%
330-6626 1
 
< 0.1%
391-8027 1
 
< 0.1%
355-9993 1
 
< 0.1%
329-9001 1
 
< 0.1%
335-4719 1
 
< 0.1%
Other values (3311) 3311
99.7%

Most occurring characters

ValueCountFrequency (%)
3 4607
17.3%
- 3321
12.5%
4 2814
10.6%
9 2079
7.8%
6 2060
7.8%
5 2046
7.7%
7 2032
7.6%
8 1996
7.5%
1 1976
7.4%
2 1882
7.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 23247
87.5%
Dash Punctuation 3321
 
12.5%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
3 4607
19.8%
4 2814
12.1%
9 2079
8.9%
6 2060
8.9%
5 2046
8.8%
7 2032
8.7%
8 1996
8.6%
1 1976
8.5%
2 1882
8.1%
0 1755
 
7.5%
Dash Punctuation
ValueCountFrequency (%)
- 3321
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 26568
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
3 4607
17.3%
- 3321
12.5%
4 2814
10.6%
9 2079
7.8%
6 2060
7.8%
5 2046
7.7%
7 2032
7.6%
8 1996
7.5%
1 1976
7.4%
2 1882
7.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 26568
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
3 4607
17.3%
- 3321
12.5%
4 2814
10.6%
9 2079
7.8%
6 2060
7.8%
5 2046
7.7%
7 2032
7.6%
8 1996
7.5%
1 1976
7.4%
2 1882
7.1%

Int'l Plan
Boolean

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size3.4 KiB
False
2998 
True
323 
ValueCountFrequency (%)
False 2998
90.3%
True 323
 
9.7%
2023-02-14T18:04:14.215356image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

VMail Plan
Boolean

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size3.4 KiB
False
2401 
True
920 
ValueCountFrequency (%)
False 2401
72.3%
True 920
 
27.7%
2023-02-14T18:04:14.340577image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

VMail Message
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct51
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.0651942
Minimum0
Maximum51
Zeros2401
Zeros (%)72.3%
Negative0
Negative (%)0.0%
Memory size26.1 KiB
2023-02-14T18:04:14.435768image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q319
95-th percentile36
Maximum51
Range51
Interquartile range (IQR)19

Descriptive statistics

Standard deviation13.669278
Coefficient of variation (CV)1.6948479
Kurtosis-0.032000519
Mean8.0651942
Median Absolute Deviation (MAD)0
Skewness1.2718263
Sum26784.51
Variance186.84916
MonotonicityNot monotonic
2023-02-14T18:04:14.532955image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 2401
72.3%
31 60
 
1.8%
29 53
 
1.6%
28 51
 
1.5%
33 45
 
1.4%
30 44
 
1.3%
27 43
 
1.3%
24 41
 
1.2%
32 41
 
1.2%
26 40
 
1.2%
Other values (41) 502
 
15.1%
ValueCountFrequency (%)
0 2401
72.3%
0.24 1
 
< 0.1%
0.25 1
 
< 0.1%
0.26 1
 
< 0.1%
0.37 1
 
< 0.1%
0.39 1
 
< 0.1%
4 1
 
< 0.1%
8 2
 
0.1%
9 2
 
0.1%
10 1
 
< 0.1%
ValueCountFrequency (%)
51 1
 
< 0.1%
50 2
 
0.1%
49 1
 
< 0.1%
48 2
 
0.1%
47 3
 
0.1%
46 4
 
0.1%
45 6
 
0.2%
44 7
0.2%
43 9
0.3%
42 15
0.5%

Day Mins
Real number (ℝ)

Distinct1671
Distinct (%)50.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean178.9386
Minimum0
Maximum350.8
Zeros2
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size26.1 KiB
2023-02-14T18:04:14.652636image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile87.7
Q1143.3
median179.2
Q3216.2
95-th percentile270.5
Maximum350.8
Range350.8
Interquartile range (IQR)72.9

Descriptive statistics

Standard deviation55.570903
Coefficient of variation (CV)0.3105585
Kurtosis0.18682769
Mean178.9386
Median Absolute Deviation (MAD)36.6
Skewness-0.13636003
Sum594255.1
Variance3088.1253
MonotonicityNot monotonic
2023-02-14T18:04:14.743273image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
174.5 8
 
0.2%
159.5 8
 
0.2%
154 8
 
0.2%
175.4 7
 
0.2%
162.3 7
 
0.2%
183.4 7
 
0.2%
181.5 6
 
0.2%
189.3 6
 
0.2%
142.3 6
 
0.2%
168.6 6
 
0.2%
Other values (1661) 3252
97.9%
ValueCountFrequency (%)
0 2
0.1%
0.811 1
< 0.1%
1.104 1
< 0.1%
1.243 1
< 0.1%
1.57 1
< 0.1%
1.616 1
< 0.1%
1.667 1
< 0.1%
1.83 1
< 0.1%
1.845 1
< 0.1%
2.13 1
< 0.1%
ValueCountFrequency (%)
350.8 1
< 0.1%
346.8 1
< 0.1%
345.3 1
< 0.1%
337.4 1
< 0.1%
335.5 1
< 0.1%
334.3 1
< 0.1%
329.8 1
< 0.1%
328.1 1
< 0.1%
326.5 1
< 0.1%
326.3 1
< 0.1%

Day Calls
Real number (ℝ)

Distinct133
Distinct (%)4.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean100.03359
Minimum0
Maximum165
Zeros2
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size26.1 KiB
2023-02-14T18:04:14.849609image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile66
Q187
median101
Q3114
95-th percentile133
Maximum165
Range165
Interquartile range (IQR)27

Descriptive statistics

Standard deviation21.032918
Coefficient of variation (CV)0.21025856
Kurtosis1.7559685
Mean100.03359
Median Absolute Deviation (MAD)13
Skewness-0.48571325
Sum332211.54
Variance442.38364
MonotonicityNot monotonic
2023-02-14T18:04:14.949590image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
102 78
 
2.3%
105 75
 
2.3%
95 69
 
2.1%
107 69
 
2.1%
104 68
 
2.0%
108 67
 
2.0%
97 66
 
2.0%
106 66
 
2.0%
101 65
 
2.0%
112 65
 
2.0%
Other values (123) 2633
79.3%
ValueCountFrequency (%)
0 2
0.1%
0.71 1
< 0.1%
0.76 1
< 0.1%
0.79 1
< 0.1%
0.84 1
< 0.1%
0.86 1
< 0.1%
0.88 1
< 0.1%
0.97 1
< 0.1%
0.98 1
< 0.1%
1.03 1
< 0.1%
ValueCountFrequency (%)
165 1
 
< 0.1%
163 1
 
< 0.1%
160 1
 
< 0.1%
158 3
0.1%
157 1
 
< 0.1%
156 1
 
< 0.1%
152 1
 
< 0.1%
151 5
0.2%
150 6
0.2%
149 1
 
< 0.1%

Day Charge
Real number (ℝ)

Distinct1670
Distinct (%)50.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean30.430891
Minimum0
Maximum59.64
Zeros2
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size26.1 KiB
2023-02-14T18:04:15.054405image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile14.91
Q124.36
median30.46
Q336.75
95-th percentile45.99
Maximum59.64
Range59.64
Interquartile range (IQR)12.39

Descriptive statistics

Standard deviation9.4331602
Coefficient of variation (CV)0.30998632
Kurtosis0.17532409
Mean30.430891
Median Absolute Deviation (MAD)6.22
Skewness-0.13059008
Sum101060.99
Variance88.984511
MonotonicityNot monotonic
2023-02-14T18:04:15.149589image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
29.67 8
 
0.2%
27.12 8
 
0.2%
26.18 8
 
0.2%
31.18 7
 
0.2%
27.59 7
 
0.2%
29.82 7
 
0.2%
36.72 6
 
0.2%
33.49 6
 
0.2%
36.65 6
 
0.2%
35.17 6
 
0.2%
Other values (1660) 3252
97.9%
ValueCountFrequency (%)
0 2
0.1%
0.1379 1
< 0.1%
0.1877 1
< 0.1%
0.2113 1
< 0.1%
0.2669 1
< 0.1%
0.2747 1
< 0.1%
0.2834 1
< 0.1%
0.3111 1
< 0.1%
0.3137 1
< 0.1%
0.3709 1
< 0.1%
ValueCountFrequency (%)
59.64 1
< 0.1%
58.96 1
< 0.1%
58.7 1
< 0.1%
57.36 1
< 0.1%
57.04 1
< 0.1%
56.83 1
< 0.1%
56.07 1
< 0.1%
55.78 1
< 0.1%
55.51 1
< 0.1%
55.47 1
< 0.1%

Eve Mins
Real number (ℝ)

Distinct1612
Distinct (%)48.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean200.10284
Minimum0
Maximum363.7
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size26.1 KiB
2023-02-14T18:04:15.248124image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile117.9
Q1165.9
median201
Q3235.1
95-th percentile283.3
Maximum363.7
Range363.7
Interquartile range (IQR)69.2

Descriptive statistics

Standard deviation51.951647
Coefficient of variation (CV)0.25962474
Kurtosis0.55693399
Mean200.10284
Median Absolute Deviation (MAD)34.4
Skewness-0.21532316
Sum664541.53
Variance2698.9736
MonotonicityNot monotonic
2023-02-14T18:04:15.364939image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
169.9 8
 
0.2%
209.4 7
 
0.2%
161.7 7
 
0.2%
201 7
 
0.2%
167.2 7
 
0.2%
180.5 7
 
0.2%
230.9 7
 
0.2%
195.5 6
 
0.2%
205.1 6
 
0.2%
178.6 6
 
0.2%
Other values (1602) 3253
98.0%
ValueCountFrequency (%)
0 1
< 0.1%
0.619 1
< 0.1%
0.729 1
< 0.1%
1.031 1
< 0.1%
1.212 1
< 0.1%
1.373 1
< 0.1%
1.483 1
< 0.1%
1.955 1
< 0.1%
1.974 1
< 0.1%
2.206 1
< 0.1%
ValueCountFrequency (%)
363.7 1
< 0.1%
361.8 1
< 0.1%
354.2 1
< 0.1%
350.9 1
< 0.1%
350.5 1
< 0.1%
347.3 1
< 0.1%
341.3 1
< 0.1%
339.9 1
< 0.1%
337.1 1
< 0.1%
336 1
< 0.1%

Eve Calls
Real number (ℝ)

Distinct136
Distinct (%)4.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean99.721774
Minimum0
Maximum170
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size26.1 KiB
2023-02-14T18:04:15.479900image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile66
Q187
median100
Q3114
95-th percentile133
Maximum170
Range170
Interquartile range (IQR)27

Descriptive statistics

Standard deviation20.902017
Coefficient of variation (CV)0.20960335
Kurtosis1.7404873
Mean99.721774
Median Absolute Deviation (MAD)13
Skewness-0.44167034
Sum331176.01
Variance436.89433
MonotonicityNot monotonic
2023-02-14T18:04:15.565507image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
105 80
 
2.4%
94 78
 
2.3%
108 70
 
2.1%
97 69
 
2.1%
102 69
 
2.1%
109 67
 
2.0%
88 67
 
2.0%
101 67
 
2.0%
98 66
 
2.0%
96 63
 
1.9%
Other values (126) 2625
79.0%
ValueCountFrequency (%)
0 1
< 0.1%
0.72 1
< 0.1%
0.8 1
< 0.1%
0.88 1
< 0.1%
0.94 1
< 0.1%
0.99 2
0.1%
1.01 1
< 0.1%
1.02 1
< 0.1%
1.03 1
< 0.1%
1.08 1
< 0.1%
ValueCountFrequency (%)
170 1
 
< 0.1%
168 1
 
< 0.1%
164 1
 
< 0.1%
159 1
 
< 0.1%
157 1
 
< 0.1%
156 1
 
< 0.1%
155 3
0.1%
154 2
 
0.1%
153 1
 
< 0.1%
152 6
0.2%

Eve Charge
Real number (ℝ)

Distinct1443
Distinct (%)43.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean17.008951
Minimum0
Maximum30.91
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size26.1 KiB
2023-02-14T18:04:15.683513image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile10.02
Q114.1
median17.09
Q319.98
95-th percentile24.08
Maximum30.91
Range30.91
Interquartile range (IQR)5.88

Descriptive statistics

Standard deviation4.4158892
Coefficient of variation (CV)0.25962149
Kurtosis0.55683893
Mean17.008951
Median Absolute Deviation (MAD)2.93
Skewness-0.21531087
Sum56486.727
Variance19.500078
MonotonicityNot monotonic
2023-02-14T18:04:15.899525image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
16.12 11
 
0.3%
14.25 11
 
0.3%
15.9 10
 
0.3%
17.09 9
 
0.3%
17.99 9
 
0.3%
18.62 9
 
0.3%
16.35 8
 
0.2%
16.97 8
 
0.2%
16.63 8
 
0.2%
17.43 8
 
0.2%
Other values (1433) 3230
97.3%
ValueCountFrequency (%)
0 1
< 0.1%
0.0526 1
< 0.1%
0.062 1
< 0.1%
0.0876 1
< 0.1%
0.103 1
< 0.1%
0.1167 1
< 0.1%
0.1261 1
< 0.1%
0.1662 1
< 0.1%
0.1678 1
< 0.1%
0.1875 1
< 0.1%
ValueCountFrequency (%)
30.91 1
< 0.1%
30.75 1
< 0.1%
30.11 1
< 0.1%
29.83 1
< 0.1%
29.79 1
< 0.1%
29.52 1
< 0.1%
29.01 1
< 0.1%
28.89 1
< 0.1%
28.65 1
< 0.1%
28.56 1
< 0.1%

Night Mins
Real number (ℝ)

Distinct1601
Distinct (%)48.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean200.04955
Minimum1.626
Maximum395
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size26.1 KiB
2023-02-14T18:04:15.994649image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum1.626
5-th percentile116.3
Q1166.7
median200.9
Q3235.3
95-th percentile282.8
Maximum395
Range393.374
Interquartile range (IQR)68.6

Descriptive statistics

Standard deviation52.127482
Coefficient of variation (CV)0.26057285
Kurtosis0.60317588
Mean200.04955
Median Absolute Deviation (MAD)34.4
Skewness-0.17860219
Sum664364.55
Variance2717.2743
MonotonicityNot monotonic
2023-02-14T18:04:16.076994image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
188.2 8
 
0.2%
210 8
 
0.2%
191.4 8
 
0.2%
197.4 8
 
0.2%
214.6 8
 
0.2%
206.1 7
 
0.2%
193.6 7
 
0.2%
214.7 7
 
0.2%
194.3 7
 
0.2%
231.5 7
 
0.2%
Other values (1591) 3246
97.7%
ValueCountFrequency (%)
1.626 1
< 0.1%
1.818 1
< 0.1%
1.869 1
< 0.1%
1.896 1
< 0.1%
1.969 1
< 0.1%
2.039 1
< 0.1%
2.118 1
< 0.1%
2.126 1
< 0.1%
2.158 1
< 0.1%
2.37 1
< 0.1%
ValueCountFrequency (%)
395 1
< 0.1%
381.9 1
< 0.1%
377.5 1
< 0.1%
367.7 1
< 0.1%
364.9 1
< 0.1%
364.3 1
< 0.1%
354.9 1
< 0.1%
352.5 1
< 0.1%
352.2 1
< 0.1%
350.2 1
< 0.1%

Night Calls
Real number (ℝ)

Distinct132
Distinct (%)4.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean99.663776
Minimum0.78
Maximum175
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size26.1 KiB
2023-02-14T18:04:16.185130image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0.78
5-th percentile67
Q186
median100
Q3113
95-th percentile132
Maximum175
Range174.22
Interquartile range (IQR)27

Descriptive statistics

Standard deviation20.56565
Coefficient of variation (CV)0.2063503
Kurtosis1.6384712
Mean99.663776
Median Absolute Deviation (MAD)13
Skewness-0.37979559
Sum330983.4
Variance422.94597
MonotonicityNot monotonic
2023-02-14T18:04:16.280489image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
105 83
 
2.5%
104 77
 
2.3%
91 75
 
2.3%
102 72
 
2.2%
100 69
 
2.1%
106 69
 
2.1%
98 67
 
2.0%
94 65
 
2.0%
95 64
 
1.9%
103 64
 
1.9%
Other values (122) 2616
78.8%
ValueCountFrequency (%)
0.78 1
< 0.1%
0.89 1
< 0.1%
0.9 1
< 0.1%
0.91 1
< 0.1%
0.96 1
< 0.1%
0.97 1
< 0.1%
1.03 1
< 0.1%
1.04 1
< 0.1%
1.05 1
< 0.1%
1.15 2
0.1%
ValueCountFrequency (%)
175 1
 
< 0.1%
166 1
 
< 0.1%
164 1
 
< 0.1%
158 1
 
< 0.1%
157 2
0.1%
156 2
0.1%
155 2
0.1%
154 2
0.1%
153 3
0.1%
152 3
0.1%

Night Charge
Real number (ℝ)

Distinct945
Distinct (%)28.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.0023047
Minimum0.0732
Maximum17.77
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size26.1 KiB
2023-02-14T18:04:16.433645image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0.0732
5-th percentile5.23
Q17.5
median9.04
Q310.59
95-th percentile12.73
Maximum17.77
Range17.6968
Interquartile range (IQR)3.09

Descriptive statistics

Standard deviation2.3457855
Coefficient of variation (CV)0.2605761
Kurtosis0.60299667
Mean9.0023047
Median Absolute Deviation (MAD)1.55
Skewness-0.17862407
Sum29896.654
Variance5.5027096
MonotonicityNot monotonic
2023-02-14T18:04:16.532814image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
9.45 15
 
0.5%
9.66 15
 
0.5%
8.88 14
 
0.4%
8.47 14
 
0.4%
7.69 13
 
0.4%
8.64 12
 
0.4%
9.32 11
 
0.3%
9.23 11
 
0.3%
8.57 11
 
0.3%
9.48 11
 
0.3%
Other values (935) 3194
96.2%
ValueCountFrequency (%)
0.0732 1
< 0.1%
0.0818 1
< 0.1%
0.0841 1
< 0.1%
0.0853 1
< 0.1%
0.0886 1
< 0.1%
0.0918 1
< 0.1%
0.0953 1
< 0.1%
0.0957 1
< 0.1%
0.0971 1
< 0.1%
0.1067 1
< 0.1%
ValueCountFrequency (%)
17.77 1
< 0.1%
17.19 1
< 0.1%
16.99 1
< 0.1%
16.55 1
< 0.1%
16.42 1
< 0.1%
16.39 1
< 0.1%
15.97 1
< 0.1%
15.86 1
< 0.1%
15.85 1
< 0.1%
15.76 1
< 0.1%

Intl Mins
Real number (ℝ)

Distinct176
Distinct (%)5.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.195984
Minimum0
Maximum20
Zeros18
Zeros (%)0.5%
Negative0
Negative (%)0.0%
Memory size26.1 KiB
2023-02-14T18:04:16.644285image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile5.6
Q18.5
median10.3
Q312.1
95-th percentile14.7
Maximum20
Range20
Interquartile range (IQR)3.6

Descriptive statistics

Standard deviation2.8630831
Coefficient of variation (CV)0.28080497
Kurtosis0.90370888
Mean10.195984
Median Absolute Deviation (MAD)1.8
Skewness-0.37109474
Sum33860.864
Variance8.197245
MonotonicityNot monotonic
2023-02-14T18:04:16.766116image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10 60
 
1.8%
11.3 59
 
1.8%
10.9 56
 
1.7%
9.8 56
 
1.7%
10.2 53
 
1.6%
11.1 52
 
1.6%
10.6 52
 
1.6%
10.1 52
 
1.6%
11 52
 
1.6%
11.4 51
 
1.5%
Other values (166) 2778
83.6%
ValueCountFrequency (%)
0 18
0.5%
0.063 1
 
< 0.1%
0.066 1
 
< 0.1%
0.071 1
 
< 0.1%
0.075 1
 
< 0.1%
0.077 1
 
< 0.1%
0.087 1
 
< 0.1%
0.095 1
 
< 0.1%
0.1 1
 
< 0.1%
0.101 1
 
< 0.1%
ValueCountFrequency (%)
20 1
 
< 0.1%
18.9 1
 
< 0.1%
18.4 1
 
< 0.1%
18.3 1
 
< 0.1%
18.2 2
0.1%
18 3
0.1%
17.9 1
 
< 0.1%
17.8 2
0.1%
17.6 2
0.1%
17.5 3
0.1%

Intl Calls
Real number (ℝ)

Distinct27
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.454324
Minimum0
Maximum20
Zeros18
Zeros (%)0.5%
Negative0
Negative (%)0.0%
Memory size26.1 KiB
2023-02-14T18:04:16.868785image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q13
median4
Q36
95-th percentile9
Maximum20
Range20
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.4646965
Coefficient of variation (CV)0.55332673
Kurtosis2.7969582
Mean4.454324
Median Absolute Deviation (MAD)1
Skewness1.2634905
Sum14792.81
Variance6.074729
MonotonicityNot monotonic
2023-02-14T18:04:16.950219image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
3 664
20.0%
4 616
18.5%
2 486
14.6%
5 466
14.0%
6 330
9.9%
7 216
 
6.5%
1 160
 
4.8%
8 116
 
3.5%
9 108
 
3.3%
10 50
 
1.5%
Other values (17) 109
 
3.3%
ValueCountFrequency (%)
0 18
 
0.5%
0.02 1
 
< 0.1%
0.03 3
 
0.1%
0.04 1
 
< 0.1%
0.05 3
 
0.1%
0.06 3
 
0.1%
0.07 2
 
0.1%
0.19 1
 
< 0.1%
1 160
 
4.8%
2 486
14.6%
ValueCountFrequency (%)
20 1
 
< 0.1%
18 3
 
0.1%
17 1
 
< 0.1%
16 2
 
0.1%
15 7
 
0.2%
14 6
 
0.2%
13 14
 
0.4%
12 15
 
0.5%
11 28
0.8%
10 50
1.5%

Intl Charge
Real number (ℝ)

Distinct176
Distinct (%)5.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.7534232
Minimum0
Maximum5.4
Zeros18
Zeros (%)0.5%
Negative0
Negative (%)0.0%
Memory size26.1 KiB
2023-02-14T18:04:17.043084image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1.51
Q12.3
median2.78
Q33.27
95-th percentile3.97
Maximum5.4
Range5.4
Interquartile range (IQR)0.97

Descriptive statistics

Standard deviation0.77301683
Coefficient of variation (CV)0.28074756
Kurtosis0.90441555
Mean2.7534232
Median Absolute Deviation (MAD)0.49
Skewness-0.37135501
Sum9144.1184
Variance0.59755502
MonotonicityNot monotonic
2023-02-14T18:04:17.137684image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2.7 60
 
1.8%
3.05 59
 
1.8%
2.94 56
 
1.7%
2.65 56
 
1.7%
2.75 53
 
1.6%
3 52
 
1.6%
2.86 52
 
1.6%
2.73 52
 
1.6%
2.97 52
 
1.6%
3.08 51
 
1.5%
Other values (166) 2778
83.6%
ValueCountFrequency (%)
0 18
0.5%
0.017 1
 
< 0.1%
0.0178 1
 
< 0.1%
0.0192 1
 
< 0.1%
0.0203 1
 
< 0.1%
0.0208 1
 
< 0.1%
0.0235 1
 
< 0.1%
0.0257 1
 
< 0.1%
0.027 1
 
< 0.1%
0.0273 1
 
< 0.1%
ValueCountFrequency (%)
5.4 1
 
< 0.1%
5.1 1
 
< 0.1%
4.97 1
 
< 0.1%
4.94 1
 
< 0.1%
4.91 2
0.1%
4.86 3
0.1%
4.83 1
 
< 0.1%
4.81 2
0.1%
4.75 2
0.1%
4.73 3
0.1%

CustServ Calls
Real number (ℝ)

Distinct13
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.5556037
Minimum0
Maximum9
Zeros695
Zeros (%)20.9%
Negative0
Negative (%)0.0%
Memory size26.1 KiB
2023-02-14T18:04:17.217199image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median1
Q32
95-th percentile4
Maximum9
Range9
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.3145967
Coefficient of variation (CV)0.8450717
Kurtosis1.7614146
Mean1.5556037
Median Absolute Deviation (MAD)1
Skewness1.0982457
Sum5166.16
Variance1.7281645
MonotonicityNot monotonic
2023-02-14T18:04:17.296376image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
1 1174
35.4%
2 757
22.8%
0 695
20.9%
3 424
 
12.8%
4 163
 
4.9%
5 65
 
2.0%
6 22
 
0.7%
7 9
 
0.3%
0.01 3
 
0.1%
0.03 3
 
0.1%
Other values (3) 6
 
0.2%
ValueCountFrequency (%)
0 695
20.9%
0.01 3
 
0.1%
0.02 2
 
0.1%
0.03 3
 
0.1%
1 1174
35.4%
2 757
22.8%
3 424
 
12.8%
4 163
 
4.9%
5 65
 
2.0%
6 22
 
0.7%
ValueCountFrequency (%)
9 2
 
0.1%
8 2
 
0.1%
7 9
 
0.3%
6 22
 
0.7%
5 65
 
2.0%
4 163
 
4.9%
3 424
 
12.8%
2 757
22.8%
1 1174
35.4%
0.03 3
 
0.1%

Churn?
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size26.1 KiB
False.
2841 
True.
480 

Length

Max length6
Median length6
Mean length5.8554652
Min length5

Characters and Unicode

Total characters19446
Distinct characters9
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowFalse.
2nd rowFalse.
3rd rowFalse.
4th rowFalse.
5th rowFalse.

Common Values

ValueCountFrequency (%)
False. 2841
85.5%
True. 480
 
14.5%

Length

2023-02-14T18:04:17.362707image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-02-14T18:04:17.449158image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
false 2841
85.5%
true 480
 
14.5%

Most occurring characters

ValueCountFrequency (%)
e 3321
17.1%
. 3321
17.1%
F 2841
14.6%
a 2841
14.6%
l 2841
14.6%
s 2841
14.6%
T 480
 
2.5%
r 480
 
2.5%
u 480
 
2.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 12804
65.8%
Other Punctuation 3321
 
17.1%
Uppercase Letter 3321
 
17.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 3321
25.9%
a 2841
22.2%
l 2841
22.2%
s 2841
22.2%
r 480
 
3.7%
u 480
 
3.7%
Uppercase Letter
ValueCountFrequency (%)
F 2841
85.5%
T 480
 
14.5%
Other Punctuation
ValueCountFrequency (%)
. 3321
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 16125
82.9%
Common 3321
 
17.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 3321
20.6%
F 2841
17.6%
a 2841
17.6%
l 2841
17.6%
s 2841
17.6%
T 480
 
3.0%
r 480
 
3.0%
u 480
 
3.0%
Common
ValueCountFrequency (%)
. 3321
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 19446
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 3321
17.1%
. 3321
17.1%
F 2841
14.6%
a 2841
14.6%
l 2841
14.6%
s 2841
14.6%
T 480
 
2.5%
r 480
 
2.5%
u 480
 
2.5%

Interactions

2023-02-14T18:04:11.083173image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T18:03:49.216769image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T18:03:50.559096image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T18:03:51.911803image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T18:03:53.362186image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T18:03:54.730388image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
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2023-02-14T18:04:04.681701image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T18:04:06.223708image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T18:04:07.634468image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T18:04:09.237432image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T18:04:10.641445image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T18:04:12.558014image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T18:03:50.317777image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T18:03:51.660344image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T18:03:53.118691image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T18:03:54.482804image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T18:03:55.941343image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T18:03:57.453695image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T18:03:58.828059image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T18:04:00.247038image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T18:04:01.810146image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T18:04:03.332359image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T18:04:04.770411image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T18:04:06.301630image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T18:04:07.735057image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T18:04:09.328680image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T18:04:10.767912image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T18:04:12.642900image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T18:03:50.405415image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T18:03:51.754602image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T18:03:53.210242image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T18:03:54.571633image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T18:03:56.034748image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T18:03:57.538607image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T18:03:58.916325image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T18:04:00.338002image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T18:04:01.907922image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T18:04:03.419251image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T18:04:04.991852image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T18:04:06.404074image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T18:04:07.824627image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T18:04:09.421839image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T18:04:10.896187image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T18:04:12.728302image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T18:03:50.485088image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T18:03:51.833676image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T18:03:53.287204image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T18:03:54.651718image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T18:03:56.115698image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T18:03:57.627705image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T18:03:58.996583image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T18:04:00.418725image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T18:04:02.011369image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T18:04:03.510458image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T18:04:05.066944image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T18:04:06.486543image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T18:04:07.907551image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T18:04:09.506073image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T18:04:10.992510image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Correlations

2023-02-14T18:04:17.524457image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Account LengthArea CodeVMail MessageDay MinsDay CallsDay ChargeEve MinsEve CallsEve ChargeNight MinsNight CallsNight ChargeIntl MinsIntl CallsIntl ChargeCustServ CallsStateInt'l PlanVMail PlanChurn?
Account Length1.000-0.0040.0050.0310.0460.0290.0040.0310.004-0.0020.002-0.0020.0280.0390.0280.0060.0000.0190.0000.000
Area Code-0.0041.0000.0110.0230.0100.0210.0160.0080.0160.0220.0340.0220.0170.0080.0170.0420.0000.0660.0000.000
VMail Message0.0050.0111.0000.001-0.0120.0020.020-0.0070.0200.0050.0130.005-0.0020.008-0.002-0.0170.0000.0170.9940.106
Day Mins0.0310.0230.0011.0000.0220.9990.0190.0290.0190.0080.0360.008-0.0010.012-0.001-0.0030.0000.0730.0330.356
Day Calls0.0460.010-0.0120.0221.0000.0220.0000.0240.0000.033-0.0050.0330.0250.0190.025-0.0100.0090.0640.0000.044
Day Charge0.0290.0210.0020.9990.0221.0000.0190.0290.0190.0080.0370.008-0.0020.011-0.002-0.0040.0000.0730.0320.356
Eve Mins0.0040.0160.0200.0190.0000.0191.0000.0041.000-0.0020.013-0.0020.0070.0260.007-0.0120.0000.0640.0070.086
Eve Calls0.0310.008-0.0070.0290.0240.0290.0041.0000.0050.0160.0190.0160.0140.0270.0140.0110.0400.0370.0000.000
Eve Charge0.0040.0160.0200.0190.0000.0191.0000.0051.000-0.0020.013-0.0020.0070.0260.007-0.0120.0000.0620.0090.086
Night Mins-0.0020.0220.0050.0080.0330.008-0.0020.016-0.0021.0000.0221.0000.0010.0130.001-0.0030.0000.0650.0000.045
Night Calls0.0020.0340.0130.036-0.0050.0370.0130.0190.0130.0221.0000.0220.0060.0100.006-0.0010.0000.0370.0000.000
Night Charge-0.0020.0220.0050.0080.0330.008-0.0020.016-0.0021.0000.0221.0000.0010.0130.001-0.0030.0000.0640.0000.046
Intl Mins0.0280.017-0.002-0.0010.025-0.0020.0070.0140.0070.0010.0060.0011.0000.0341.000-0.0090.0000.0380.0000.061
Intl Calls0.0390.0080.0080.0120.0190.0110.0260.0270.0260.0130.0100.0130.0341.0000.0340.0070.0000.0490.0000.092
Intl Charge0.0280.017-0.002-0.0010.025-0.0020.0070.0140.0070.0010.0060.0011.0000.0341.000-0.0090.0000.0380.0000.061
CustServ Calls0.0060.042-0.017-0.003-0.010-0.004-0.0120.011-0.012-0.003-0.001-0.003-0.0090.007-0.0091.0000.0000.0480.0120.312
State0.0000.0000.0000.0000.0090.0000.0000.0400.0000.0000.0000.0000.0000.0000.0000.0001.0000.0630.0000.100
Int'l Plan0.0190.0660.0170.0730.0640.0730.0640.0370.0620.0650.0370.0640.0380.0490.0380.0480.0631.0000.0000.259
VMail Plan0.0000.0000.9940.0330.0000.0320.0070.0000.0090.0000.0000.0000.0000.0000.0000.0120.0000.0001.0000.099
Churn?0.0000.0000.1060.3560.0440.3560.0860.0000.0860.0450.0000.0460.0610.0920.0610.3120.1000.2590.0991.000

Missing values

2023-02-14T18:04:12.870170image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
A simple visualization of nullity by column.
2023-02-14T18:04:13.243334image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

StateAccount LengthArea CodePhoneInt'l PlanVMail PlanVMail MessageDay MinsDay CallsDay ChargeEve MinsEve CallsEve ChargeNight MinsNight CallsNight ChargeIntl MinsIntl CallsIntl ChargeCustServ CallsChurn?
0KS1.284.15382-4657noyes0.252.6511.100.45071.9740.990.16782.4470.910.11010.1000.030.02700.01False.
1OH1.074.15371-7191noyes0.261.6161.230.27471.9551.030.16622.5441.030.11450.1370.030.03700.01False.
2NJ1.374.15358-1921nono0.002.4341.140.41381.2121.100.10301.6261.040.07320.1220.050.03290.00False.
3OH0.844.08375-9999yesno0.002.9940.710.50900.6190.880.05261.9690.890.08860.0660.070.01780.02False.
4OK0.754.15330-6626yesno0.001.6671.130.28341.4831.220.12611.8691.210.08410.1010.030.02730.03False.
5AL1.185.10391-8027yesno0.002.2340.980.37982.2061.010.18752.0391.180.09180.0630.060.01700.00False.
6MA1.215.10355-9993noyes0.242.1820.880.37093.4851.080.29622.1261.180.09570.0750.070.02030.03False.
7MO1.474.15329-9001yesno0.001.5700.790.26691.0310.940.08762.1180.960.09530.0710.060.01920.00False.
8LA1.174.08335-4719nono0.001.8450.970.31373.5160.800.29892.1580.900.09710.0870.040.02350.01False.
9WV1.414.15330-8173yesyes0.372.5860.840.43962.2201.110.18873.2640.970.14690.1120.050.03020.00False.
StateAccount LengthArea CodePhoneInt'l PlanVMail PlanVMail MessageDay MinsDay CallsDay ChargeEve MinsEve CallsEve ChargeNight MinsNight CallsNight ChargeIntl MinsIntl CallsIntl ChargeCustServ CallsChurn?
3311IN117.0415.0362-5899nono0.0118.4126.020.13249.397.021.19227.056.010.2213.63.03.675.0True.
3312WV159.0415.0377-1164nono0.0169.8114.028.87197.7105.016.80193.782.08.7211.64.03.131.0False.
3313OH78.0408.0368-8555nono0.0193.499.032.88116.988.09.94243.3109.010.959.34.02.512.0False.
3314OH96.0415.0347-6812nono0.0106.6128.018.12284.887.024.21178.992.08.0514.97.04.021.0False.
3315SC79.0415.0348-3830nono0.0134.798.022.90189.768.016.12221.4128.09.9611.85.03.192.0False.
3316AZ192.0415.0414-4276noyes36.0156.277.026.55215.5126.018.32279.183.012.569.96.02.672.0False.
3317WV68.0415.0370-3271nono0.0231.157.039.29153.455.013.04191.3123.08.619.64.02.593.0False.
3318RI28.0510.0328-8230nono0.0180.8109.030.74288.858.024.55191.991.08.6414.16.03.812.0False.
3319CT184.0510.0364-6381yesno0.0213.8105.036.35159.684.013.57139.2137.06.265.010.01.352.0False.
3320TN74.0415.0400-4344noyes25.0234.4113.039.85265.982.022.60241.477.010.8613.74.03.700.0False.